Mining knowledge for the methylation status of CpG islands using alternating decision trees.
Mining knowledge for the methylation status of CpG islands using alternating decision trees.
复制标题
使用交替决策树挖掘 CpG 岛甲基化状态的知识。
DOI：
10.1109/iembs.2008.4650033
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发表时间：
2008
期刊：
影响因子：
--
通讯作者：
Lu,Hui
中科院分区：
文献类型：
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作者：
Carson,MatthewB;Langlois,Robert;Lu,Hui
CpG岛（CpGI）甲基化是发生在真核生物中的表观遗传修饰，并且基于向胞嘧啶的嘧啶环的5号碳添加甲基。当CpGI发生甲基化时，相关基因（如果有的话）不表达[1]。异常甲基化被认为是疾病[2]和药物敏感性[3]，[4]的病原体。在这项工作中，我们预测了甲基化状态的CpGIs在人类21号染色体上使用序列模式。这些模式显示在先前的工作中甲基化和未甲基化的岛之间存在显著不同的分布[5]。使用C4.5和Bagging和成本敏感学习，我们实现了85.6%的准确性，82.8%的灵敏度和86.4%的特异性。然后，我们使用自举方法构建了1000棵交替决策树，并分析了树之间保守的节点。这使我们能够找到区分甲基化和非甲基化CpGI的序列模式的特定组合。对这些特征的分析提供了对允许或阻止甲基化的条件的某些了解。
CpG island (CpGI) methylation is an epigenetic modification that occurs in eukaryotes and is based on the addition of a methyl group to the number 5 carbon of the pyrimidine ring of cytosine. When methylation of a CpGI occurs, the associated gene (if any) is not expressed [1]. Aberrant methylation is thought to be a causative agent in disease [2] and drug sensitivity [3], [4]. In this work, we have predicted the methylation status of CpGIs in human chromosome 21 using sequence patterns. These patterns showed a significantly different distribution between methylated and unmethylated islands in a previous work [5]. Using C4.5 with bagging and cost-sensitive learning, we achieved 85.6% accuracy, 82.8% sensitivity, and 86.4% specificity. We then constructed 1000 alternating decision trees using a bootstrapping method and analyzed the nodes that were conserved between the trees. This allowed us to find specific combinations of sequence patterns that distinguished between methylated and unmethylated CpGIs. Analysis of these characteristics offers certain insight into the conditions that permit or prevent methylation.